Overview

Dataset statistics

Number of variables19
Number of observations5520
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory819.5 KiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Alerts

track has a high cardinality: 5311 distinct valuesHigh cardinality
artist has a high cardinality: 2476 distinct valuesHigh cardinality
uri has a high cardinality: 5506 distinct valuesHigh cardinality
danceability is highly overall correlated with valence and 1 other fieldsHigh correlation
energy is highly overall correlated with loudness and 2 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 2 other fieldsHigh correlation
valence is highly overall correlated with danceability and 1 other fieldsHigh correlation
duration_ms is highly overall correlated with chorus_hit and 2 other fieldsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
instrumentalness is highly overall correlated with targetHigh correlation
chorus_hit is highly overall correlated with duration_msHigh correlation
target is highly overall correlated with danceability and 3 other fieldsHigh correlation
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 646 (11.7%) zerosZeros
instrumentalness has 1447 (26.2%) zerosZeros

Reproduction

Analysis started2022-11-29 22:47:57.549189
Analysis finished2022-11-29 22:48:15.059788
Duration17.51 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5311
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size43.2 KiB
Clarinet Marmalade
 
6
Maria
 
5
We Will Rock You
 
4
Real Love
 
4
You
 
4
Other values (5306)
5497 

Length

Max length186
Median length103
Mean length17.89058
Min length1

Characters and Unicode

Total characters98756
Distinct characters190
Distinct categories16 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5131 ?
Unique (%)93.0%

Sample

1st rowMisty Roses
2nd rowNever Ever
3rd rowSoul Sermon
4th rowClarinet Marmalade - Live
5th rowДо смерті і довше - Drum & Base and Rock Remix

Common Values

ValueCountFrequency (%)
Clarinet Marmalade 6
 
0.1%
Maria 5
 
0.1%
We Will Rock You 4
 
0.1%
Real Love 4
 
0.1%
You 4
 
0.1%
Crazy 4
 
0.1%
Please Don't Go 4
 
0.1%
Hold On 3
 
0.1%
Give It Up 3
 
0.1%
Believe 3
 
0.1%
Other values (5301) 5480
99.3%

Length

2022-11-29T17:48:15.153737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 717
 
3.8%
you 436
 
2.3%
353
 
1.8%
i 320
 
1.7%
of 288
 
1.5%
love 274
 
1.4%
me 251
 
1.3%
a 249
 
1.3%
in 223
 
1.2%
to 222
 
1.2%
Other values (5179) 15772
82.6%

Most occurring characters

ValueCountFrequency (%)
13585
 
13.8%
e 9005
 
9.1%
o 6597
 
6.7%
a 5302
 
5.4%
n 4985
 
5.0%
i 4478
 
4.5%
r 4345
 
4.4%
t 4240
 
4.3%
l 3254
 
3.3%
s 3185
 
3.2%
Other values (180) 39780
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63353
64.2%
Uppercase Letter 17800
 
18.0%
Space Separator 13585
 
13.8%
Other Punctuation 1853
 
1.9%
Decimal Number 825
 
0.8%
Dash Punctuation 378
 
0.4%
Close Punctuation 360
 
0.4%
Open Punctuation 360
 
0.4%
Other Letter 184
 
0.2%
Nonspacing Mark 43
 
< 0.1%
Other values (6) 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9005
14.2%
o 6597
10.4%
a 5302
 
8.4%
n 4985
 
7.9%
i 4478
 
7.1%
r 4345
 
6.9%
t 4240
 
6.7%
l 3254
 
5.1%
s 3185
 
5.0%
h 2570
 
4.1%
Other values (51) 15392
24.3%
Other Letter
ValueCountFrequency (%)
17
 
9.2%
16
 
8.7%
13
 
7.1%
12
 
6.5%
10
 
5.4%
9
 
4.9%
8
 
4.3%
8
 
4.3%
7
 
3.8%
7
 
3.8%
Other values (35) 77
41.8%
Uppercase Letter
ValueCountFrequency (%)
T 1734
 
9.7%
S 1320
 
7.4%
M 1295
 
7.3%
I 1191
 
6.7%
A 1169
 
6.6%
L 1002
 
5.6%
B 958
 
5.4%
C 886
 
5.0%
W 871
 
4.9%
D 831
 
4.7%
Other values (23) 6543
36.8%
Other Punctuation
ValueCountFrequency (%)
' 731
39.4%
. 413
22.3%
, 222
 
12.0%
: 127
 
6.9%
" 125
 
6.7%
/ 84
 
4.5%
? 59
 
3.2%
& 40
 
2.2%
! 35
 
1.9%
* 6
 
0.3%
Other values (5) 11
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 172
20.8%
2 135
16.4%
9 114
13.8%
0 72
8.7%
3 72
8.7%
4 68
 
8.2%
7 53
 
6.4%
5 52
 
6.3%
6 51
 
6.2%
8 36
 
4.4%
Nonspacing Mark
ValueCountFrequency (%)
10
23.3%
8
18.6%
7
16.3%
5
11.6%
4
 
9.3%
3
 
7.0%
2
 
4.7%
2
 
4.7%
1
 
2.3%
1
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 375
99.2%
2
 
0.5%
1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 343
95.3%
] 17
 
4.7%
Open Punctuation
ValueCountFrequency (%)
( 343
95.3%
[ 17
 
4.7%
Math Symbol
ValueCountFrequency (%)
= 3
75.0%
+ 1
 
25.0%
Modifier Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
13585
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 4
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81114
82.1%
Common 17375
 
17.6%
Thai 213
 
0.2%
Cyrillic 39
 
< 0.1%
Katakana 8
 
< 0.1%
Han 5
 
< 0.1%
Hiragana 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9005
 
11.1%
o 6597
 
8.1%
a 5302
 
6.5%
n 4985
 
6.1%
i 4478
 
5.5%
r 4345
 
5.4%
t 4240
 
5.2%
l 3254
 
4.0%
s 3185
 
3.9%
h 2570
 
3.2%
Other values (65) 33153
40.9%
Thai
ValueCountFrequency (%)
17
 
8.0%
16
 
7.5%
13
 
6.1%
12
 
5.6%
10
 
4.7%
10
 
4.7%
9
 
4.2%
8
 
3.8%
8
 
3.8%
8
 
3.8%
Other values (31) 102
47.9%
Common
ValueCountFrequency (%)
13585
78.2%
' 731
 
4.2%
. 413
 
2.4%
- 375
 
2.2%
) 343
 
2.0%
( 343
 
2.0%
, 222
 
1.3%
1 172
 
1.0%
2 135
 
0.8%
: 127
 
0.7%
Other values (30) 929
 
5.3%
Cyrillic
ValueCountFrequency (%)
о 5
12.8%
а 5
12.8%
е 4
10.3%
м 3
 
7.7%
н 3
 
7.7%
р 3
 
7.7%
т 3
 
7.7%
і 2
 
5.1%
Д 1
 
2.6%
с 1
 
2.6%
Other values (9) 9
23.1%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Han
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hiragana
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98279
99.5%
Thai 213
 
0.2%
None 203
 
0.2%
Cyrillic 39
 
< 0.1%
Katakana 9
 
< 0.1%
Punctuation 6
 
< 0.1%
CJK 5
 
< 0.1%
Hiragana 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13585
 
13.8%
e 9005
 
9.2%
o 6597
 
6.7%
a 5302
 
5.4%
n 4985
 
5.1%
i 4478
 
4.6%
r 4345
 
4.4%
t 4240
 
4.3%
l 3254
 
3.3%
s 3185
 
3.2%
Other values (74) 39303
40.0%
None
ValueCountFrequency (%)
é 46
22.7%
ó 18
 
8.9%
è 16
 
7.9%
ã 15
 
7.4%
ê 14
 
6.9%
á 13
 
6.4%
ç 12
 
5.9%
í 7
 
3.4%
å 7
 
3.4%
ö 7
 
3.4%
Other values (16) 48
23.6%
Thai
ValueCountFrequency (%)
17
 
8.0%
16
 
7.5%
13
 
6.1%
12
 
5.6%
10
 
4.7%
10
 
4.7%
9
 
4.2%
8
 
3.8%
8
 
3.8%
8
 
3.8%
Other values (31) 102
47.9%
Cyrillic
ValueCountFrequency (%)
о 5
12.8%
а 5
12.8%
е 4
10.3%
м 3
 
7.7%
н 3
 
7.7%
р 3
 
7.7%
т 3
 
7.7%
і 2
 
5.1%
Д 1
 
2.6%
с 1
 
2.6%
Other values (9) 9
23.1%
Punctuation
ValueCountFrequency (%)
3
50.0%
1
 
16.7%
1
 
16.7%
1
 
16.7%
CJK
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hiragana
ValueCountFrequency (%)
1
50.0%
1
50.0%
Katakana
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

artist
Categorical

Distinct2476
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Memory size43.2 KiB
Luis Miguel
 
29
Madonna
 
25
Iggy Pop
 
22
Nobuo Uematsu
 
19
Judas Priest
 
17
Other values (2471)
5408 

Length

Max length81
Median length60
Mean length13.054891
Min length2

Characters and Unicode

Total characters72063
Distinct characters111
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1444 ?
Unique (%)26.2%

Sample

1st rowAstrud Gilberto
2nd rowAll Saints
3rd rowGregg Karukas
4th rowAlton Purnell
5th rowSkryabin

Common Values

ValueCountFrequency (%)
Luis Miguel 29
 
0.5%
Madonna 25
 
0.5%
Iggy Pop 22
 
0.4%
Nobuo Uematsu 19
 
0.3%
Judas Priest 17
 
0.3%
Ella Jenkins 17
 
0.3%
Demented Are Go 17
 
0.3%
El Gran Combo De Puerto Rico 17
 
0.3%
Mariah Carey 16
 
0.3%
Gloria Estefan 16
 
0.3%
Other values (2466) 5325
96.5%

Length

2022-11-29T17:48:15.282615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 453
 
3.7%
174
 
1.4%
featuring 152
 
1.3%
of 86
 
0.7%
john 60
 
0.5%
michael 59
 
0.5%
los 49
 
0.4%
jazz 41
 
0.3%
de 38
 
0.3%
kenny 37
 
0.3%
Other values (3387) 10970
90.5%

Most occurring characters

ValueCountFrequency (%)
e 6644
 
9.2%
6599
 
9.2%
a 5437
 
7.5%
n 4350
 
6.0%
i 4186
 
5.8%
o 4136
 
5.7%
r 4007
 
5.6%
l 3014
 
4.2%
t 2944
 
4.1%
s 2766
 
3.8%
Other values (101) 27980
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51608
71.6%
Uppercase Letter 12670
 
17.6%
Space Separator 6599
 
9.2%
Other Punctuation 699
 
1.0%
Decimal Number 230
 
0.3%
Dash Punctuation 182
 
0.3%
Close Punctuation 26
 
< 0.1%
Open Punctuation 26
 
< 0.1%
Other Letter 16
 
< 0.1%
Currency Symbol 3
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6644
12.9%
a 5437
10.5%
n 4350
 
8.4%
i 4186
 
8.1%
o 4136
 
8.0%
r 4007
 
7.8%
l 3014
 
5.8%
t 2944
 
5.7%
s 2766
 
5.4%
h 1992
 
3.9%
Other values (33) 12132
23.5%
Uppercase Letter
ValueCountFrequency (%)
M 1059
 
8.4%
S 1007
 
7.9%
B 999
 
7.9%
T 989
 
7.8%
C 924
 
7.3%
J 698
 
5.5%
A 676
 
5.3%
L 643
 
5.1%
D 614
 
4.8%
P 614
 
4.8%
Other values (20) 4447
35.1%
Other Punctuation
ValueCountFrequency (%)
. 317
45.4%
& 174
24.9%
' 129
18.5%
, 39
 
5.6%
" 16
 
2.3%
/ 9
 
1.3%
! 7
 
1.0%
? 3
 
0.4%
2
 
0.3%
* 2
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 55
23.9%
0 55
23.9%
1 26
11.3%
4 19
 
8.3%
8 16
 
7.0%
7 14
 
6.1%
5 13
 
5.7%
3 13
 
5.7%
9 10
 
4.3%
6 9
 
3.9%
Other Letter
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
Close Punctuation
ValueCountFrequency (%)
) 24
92.3%
] 2
 
7.7%
Open Punctuation
ValueCountFrequency (%)
( 24
92.3%
[ 2
 
7.7%
Space Separator
ValueCountFrequency (%)
6599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64278
89.2%
Common 7769
 
10.8%
Katakana 16
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6644
 
10.3%
a 5437
 
8.5%
n 4350
 
6.8%
i 4186
 
6.5%
o 4136
 
6.4%
r 4007
 
6.2%
l 3014
 
4.7%
t 2944
 
4.6%
s 2766
 
4.3%
h 1992
 
3.1%
Other values (63) 24802
38.6%
Common
ValueCountFrequency (%)
6599
84.9%
. 317
 
4.1%
- 182
 
2.3%
& 174
 
2.2%
' 129
 
1.7%
2 55
 
0.7%
0 55
 
0.7%
, 39
 
0.5%
1 26
 
0.3%
) 24
 
0.3%
Other values (20) 169
 
2.2%
Katakana
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71865
99.7%
None 178
 
0.2%
Katakana 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6644
 
9.2%
6599
 
9.2%
a 5437
 
7.6%
n 4350
 
6.1%
i 4186
 
5.8%
o 4136
 
5.8%
r 4007
 
5.6%
l 3014
 
4.2%
t 2944
 
4.1%
s 2766
 
3.8%
Other values (70) 27782
38.7%
None
ValueCountFrequency (%)
é 54
30.3%
ã 16
 
9.0%
ö 15
 
8.4%
í 14
 
7.9%
ô 12
 
6.7%
è 11
 
6.2%
á 9
 
5.1%
ñ 8
 
4.5%
ä 7
 
3.9%
ó 7
 
3.9%
Other values (11) 25
14.0%
Katakana
ValueCountFrequency (%)
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5506
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size43.2 KiB
spotify:track:3CO19cTl5zzlWx9GWEpOhD
 
2
spotify:track:69uJi5QsBtqlYkGURTBli8
 
2
spotify:track:1blZP5x1XQSqQFpTy12rFh
 
2
spotify:track:6wetvpPWooBdmAEOKnDhpo
 
2
spotify:track:6uImoDq9R3ZfJOPXkLdyWo
 
2
Other values (5501)
5510 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters198720
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5492 ?
Unique (%)99.5%

Sample

1st rowspotify:track:50RBM1j1Dw7WYmsGsWg9Tm
2nd rowspotify:track:5FTz9qQ94PyUHETyAyfYZN
3rd rowspotify:track:6m24oe3lk1UMxq9zq4iPFi
4th rowspotify:track:5FOXuiLI6knVtgMUjWKj6x
5th rowspotify:track:6CxyIPTqSPvAPXfrIZczs4

Common Values

ValueCountFrequency (%)
spotify:track:3CO19cTl5zzlWx9GWEpOhD 2
 
< 0.1%
spotify:track:69uJi5QsBtqlYkGURTBli8 2
 
< 0.1%
spotify:track:1blZP5x1XQSqQFpTy12rFh 2
 
< 0.1%
spotify:track:6wetvpPWooBdmAEOKnDhpo 2
 
< 0.1%
spotify:track:6uImoDq9R3ZfJOPXkLdyWo 2
 
< 0.1%
spotify:track:2drQ6wg2hdl6RDslarifh8 2
 
< 0.1%
spotify:track:7bp5DfkdK1OAvNJ1U4HfDA 2
 
< 0.1%
spotify:track:2FJiNQbxS33DkE0w5IXoxc 2
 
< 0.1%
spotify:track:4RADreHMvMkZwsPgPr9z5c 2
 
< 0.1%
spotify:track:2rVXKUyHxMoWk7vTyOcYIH 2
 
< 0.1%
Other values (5496) 5500
99.6%

Length

2022-11-29T17:48:15.374631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:3co19ctl5zzlwx9gwepohd 2
 
< 0.1%
spotify:track:4radrehmvmkzwspgpr9z5c 2
 
< 0.1%
spotify:track:69uji5qsbtqlykgurtbli8 2
 
< 0.1%
spotify:track:7okbmga8lrbgl5limz7lfm 2
 
< 0.1%
spotify:track:5gorfakkp2mlreqvhsblig 2
 
< 0.1%
spotify:track:3xsu3s8v206qwhob9n2lgj 2
 
< 0.1%
spotify:track:2rvxkuyhxmowk7vtyocyih 2
 
< 0.1%
spotify:track:5nhq2ypvcddwjz71aupuvz 2
 
< 0.1%
spotify:track:2fjinqbxs33dke0w5ixoxc 2
 
< 0.1%
spotify:track:7bp5dfkdk1oavnj1u4hfda 2
 
< 0.1%
Other values (5496) 5500
99.6%

Most occurring characters

ValueCountFrequency (%)
t 12898
 
6.5%
: 11040
 
5.6%
p 7470
 
3.8%
s 7444
 
3.7%
y 7421
 
3.7%
a 7410
 
3.7%
o 7406
 
3.7%
c 7395
 
3.7%
k 7365
 
3.7%
i 7338
 
3.7%
Other values (53) 115533
58.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 114898
57.8%
Uppercase Letter 48750
24.5%
Decimal Number 24032
 
12.1%
Other Punctuation 11040
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12898
 
11.2%
p 7470
 
6.5%
s 7444
 
6.5%
y 7421
 
6.5%
a 7410
 
6.4%
o 7406
 
6.4%
c 7395
 
6.4%
k 7365
 
6.4%
i 7338
 
6.4%
r 7330
 
6.4%
Other values (16) 35421
30.8%
Uppercase Letter
ValueCountFrequency (%)
K 1961
 
4.0%
J 1951
 
4.0%
A 1919
 
3.9%
P 1907
 
3.9%
E 1905
 
3.9%
Y 1905
 
3.9%
F 1898
 
3.9%
Q 1892
 
3.9%
R 1891
 
3.9%
N 1891
 
3.9%
Other values (16) 29630
60.8%
Decimal Number
ValueCountFrequency (%)
1 2658
11.1%
3 2605
10.8%
5 2593
10.8%
4 2564
10.7%
0 2558
10.6%
2 2547
10.6%
6 2475
10.3%
7 2344
9.8%
9 1881
7.8%
8 1807
7.5%
Other Punctuation
ValueCountFrequency (%)
: 11040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 163648
82.4%
Common 35072
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12898
 
7.9%
p 7470
 
4.6%
s 7444
 
4.5%
y 7421
 
4.5%
a 7410
 
4.5%
o 7406
 
4.5%
c 7395
 
4.5%
k 7365
 
4.5%
i 7338
 
4.5%
r 7330
 
4.5%
Other values (42) 84171
51.4%
Common
ValueCountFrequency (%)
: 11040
31.5%
1 2658
 
7.6%
3 2605
 
7.4%
5 2593
 
7.4%
4 2564
 
7.3%
0 2558
 
7.3%
2 2547
 
7.3%
6 2475
 
7.1%
7 2344
 
6.7%
9 1881
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 12898
 
6.5%
: 11040
 
5.6%
p 7470
 
3.8%
s 7444
 
3.7%
y 7421
 
3.7%
a 7410
 
3.7%
o 7406
 
3.7%
c 7395
 
3.7%
k 7365
 
3.7%
i 7338
 
3.7%
Other values (53) 115533
58.1%

danceability
Real number (ℝ)

Distinct838
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56689306
Minimum0.0576
Maximum0.979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:15.456464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0576
5-th percentile0.23495
Q10.451
median0.586
Q30.699
95-th percentile0.83705
Maximum0.979
Range0.9214
Interquartile range (IQR)0.248

Descriptive statistics

Standard deviation0.18037311
Coefficient of variation (CV)0.31817837
Kurtosis-0.35985145
Mean0.56689306
Median Absolute Deviation (MAD)0.122
Skewness-0.39095701
Sum3129.2497
Variance0.032534459
MonotonicityNot monotonic
2022-11-29T17:48:15.556917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.613 22
 
0.4%
0.607 22
 
0.4%
0.547 20
 
0.4%
0.69 20
 
0.4%
0.567 19
 
0.3%
0.723 19
 
0.3%
0.537 18
 
0.3%
0.648 18
 
0.3%
0.621 18
 
0.3%
0.581 18
 
0.3%
Other values (828) 5326
96.5%
ValueCountFrequency (%)
0.0576 1
< 0.1%
0.0596 2
< 0.1%
0.0617 1
< 0.1%
0.062 1
< 0.1%
0.0629 1
< 0.1%
0.063 1
< 0.1%
0.0632 1
< 0.1%
0.0665 1
< 0.1%
0.0724 1
< 0.1%
0.0752 1
< 0.1%
ValueCountFrequency (%)
0.979 1
< 0.1%
0.976 1
< 0.1%
0.966 1
< 0.1%
0.965 1
< 0.1%
0.959 1
< 0.1%
0.956 1
< 0.1%
0.953 1
< 0.1%
0.951 1
< 0.1%
0.948 1
< 0.1%
0.947 1
< 0.1%

energy
Real number (ℝ)

Distinct1098
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60224639
Minimum0.000357
Maximum0.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:15.652267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000357
5-th percentile0.11795
Q10.435
median0.6345
Q30.811
95-th percentile0.952
Maximum0.998
Range0.997643
Interquartile range (IQR)0.376

Descriptive statistics

Standard deviation0.25218922
Coefficient of variation (CV)0.41874758
Kurtosis-0.62319644
Mean0.60224639
Median Absolute Deviation (MAD)0.1865
Skewness-0.46410344
Sum3324.4001
Variance0.063599404
MonotonicityNot monotonic
2022-11-29T17:48:15.754775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.727 20
 
0.4%
0.942 19
 
0.3%
0.961 17
 
0.3%
0.731 16
 
0.3%
0.458 16
 
0.3%
0.72 16
 
0.3%
0.745 15
 
0.3%
0.495 15
 
0.3%
0.505 15
 
0.3%
0.883 14
 
0.3%
Other values (1088) 5357
97.0%
ValueCountFrequency (%)
0.000357 1
< 0.1%
0.000419 1
< 0.1%
0.000707 1
< 0.1%
0.00093 1
< 0.1%
0.00101 1
< 0.1%
0.00167 1
< 0.1%
0.00196 1
< 0.1%
0.00198 1
< 0.1%
0.00217 1
< 0.1%
0.00237 1
< 0.1%
ValueCountFrequency (%)
0.998 1
 
< 0.1%
0.997 5
0.1%
0.996 3
0.1%
0.995 5
0.1%
0.994 5
0.1%
0.993 7
0.1%
0.992 4
0.1%
0.991 5
0.1%
0.99 6
0.1%
0.989 5
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2630435
Minimum0
Maximum11
Zeros646
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:15.836357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5113788
Coefficient of variation (CV)0.66717647
Kurtosis-1.2536374
Mean5.2630435
Median Absolute Deviation (MAD)3
Skewness-0.013302003
Sum29052
Variance12.329781
MonotonicityNot monotonic
2022-11-29T17:48:15.905907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 709
12.8%
0 646
11.7%
2 563
10.2%
9 563
10.2%
1 491
8.9%
5 489
8.9%
4 423
7.7%
11 384
7.0%
6 373
6.8%
10 372
6.7%
Other values (2) 507
9.2%
ValueCountFrequency (%)
0 646
11.7%
1 491
8.9%
2 563
10.2%
3 194
 
3.5%
4 423
7.7%
5 489
8.9%
6 373
6.8%
7 709
12.8%
8 313
5.7%
9 563
10.2%
ValueCountFrequency (%)
11 384
7.0%
10 372
6.7%
9 563
10.2%
8 313
5.7%
7 709
12.8%
6 373
6.8%
5 489
8.9%
4 423
7.7%
3 194
 
3.5%
2 563
10.2%

loudness
Real number (ℝ)

Distinct4529
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.235112
Minimum-49.253
Maximum-1.169
Zeros0
Zeros (%)0.0%
Negative5520
Negative (%)100.0%
Memory size43.2 KiB
2022-11-29T17:48:15.990374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-49.253
5-th percentile-20.4042
Q1-12.20725
median-9.091
Q3-6.89375
95-th percentile-4.41395
Maximum-1.169
Range48.084
Interquartile range (IQR)5.3135

Descriptive statistics

Standard deviation5.1171458
Coefficient of variation (CV)-0.49995994
Kurtosis5.6653935
Mean-10.235112
Median Absolute Deviation (MAD)2.511
Skewness-1.8649413
Sum-56497.816
Variance26.185181
MonotonicityNot monotonic
2022-11-29T17:48:16.092032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.896 5
 
0.1%
-10.418 5
 
0.1%
-7.878 5
 
0.1%
-7.017 4
 
0.1%
-8.831 4
 
0.1%
-8.333 4
 
0.1%
-9.518 4
 
0.1%
-6.725 4
 
0.1%
-6.48 4
 
0.1%
-6.705 4
 
0.1%
Other values (4519) 5477
99.2%
ValueCountFrequency (%)
-49.253 1
< 0.1%
-43.989 1
< 0.1%
-43.06 1
< 0.1%
-42.959 1
< 0.1%
-42.66 1
< 0.1%
-40.561 1
< 0.1%
-40.26 1
< 0.1%
-40.147 1
< 0.1%
-38.499 1
< 0.1%
-37.867 1
< 0.1%
ValueCountFrequency (%)
-1.169 1
< 0.1%
-1.228 1
< 0.1%
-1.555 1
< 0.1%
-1.97 1
< 0.1%
-2.217 1
< 0.1%
-2.345 1
< 0.1%
-2.349 1
< 0.1%
-2.383 1
< 0.1%
-2.41 1
< 0.1%
-2.481 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.2 KiB
1
3696 
0
1824 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3696
67.0%
0 1824
33.0%

Length

2022-11-29T17:48:16.183731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:48:16.255362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3696
67.0%
0 1824
33.0%

Most occurring characters

ValueCountFrequency (%)
1 3696
67.0%
0 1824
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3696
67.0%
0 1824
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3696
67.0%
0 1824
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3696
67.0%
0 1824
33.0%

speechiness
Real number (ℝ)

Distinct1017
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.073996159
Minimum0.022
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:16.326757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.022
5-th percentile0.0268
Q10.0331
median0.0432
Q30.072825
95-th percentile0.254
Maximum0.95
Range0.928
Interquartile range (IQR)0.039725

Descriptive statistics

Standard deviation0.081978664
Coefficient of variation (CV)1.1078773
Kurtosis21.858197
Mean0.073996159
Median Absolute Deviation (MAD)0.0129
Skewness3.7700362
Sum408.4588
Variance0.0067205013
MonotonicityNot monotonic
2022-11-29T17:48:16.428041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0331 32
 
0.6%
0.0283 27
 
0.5%
0.0346 26
 
0.5%
0.0363 26
 
0.5%
0.0305 25
 
0.5%
0.0302 25
 
0.5%
0.0298 25
 
0.5%
0.0341 25
 
0.5%
0.0324 24
 
0.4%
0.0295 24
 
0.4%
Other values (1007) 5261
95.3%
ValueCountFrequency (%)
0.022 1
 
< 0.1%
0.0221 1
 
< 0.1%
0.0224 2
< 0.1%
0.0225 1
 
< 0.1%
0.0228 2
< 0.1%
0.023 2
< 0.1%
0.0231 1
 
< 0.1%
0.0232 1
 
< 0.1%
0.0233 3
0.1%
0.0234 2
< 0.1%
ValueCountFrequency (%)
0.95 1
< 0.1%
0.94 1
< 0.1%
0.935 1
< 0.1%
0.912 1
< 0.1%
0.911 1
< 0.1%
0.903 1
< 0.1%
0.897 1
< 0.1%
0.883 1
< 0.1%
0.63 1
< 0.1%
0.573 1
< 0.1%

acousticness
Real number (ℝ)

Distinct2384
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29121966
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:16.527626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0001759
Q10.018
median0.14
Q30.52725
95-th percentile0.951
Maximum0.996
Range0.996
Interquartile range (IQR)0.50925

Descriptive statistics

Standard deviation0.32463046
Coefficient of variation (CV)1.1147272
Kurtosis-0.6195327
Mean0.29121966
Median Absolute Deviation (MAD)0.137935
Skewness0.90363094
Sum1607.5325
Variance0.10538494
MonotonicityNot monotonic
2022-11-29T17:48:16.629252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 24
 
0.4%
0.993 16
 
0.3%
0.994 14
 
0.3%
0.114 14
 
0.3%
0.173 12
 
0.2%
0.101 12
 
0.2%
0.102 12
 
0.2%
0.99 11
 
0.2%
0.0108 11
 
0.2%
0.175 11
 
0.2%
Other values (2374) 5383
97.5%
ValueCountFrequency (%)
0 1
< 0.1%
1.03 × 10-61
< 0.1%
1.19 × 10-61
< 0.1%
1.55 × 10-61
< 0.1%
1.57 × 10-61
< 0.1%
1.58 × 10-61
< 0.1%
1.6 × 10-61
< 0.1%
1.68 × 10-61
< 0.1%
1.83 × 10-61
< 0.1%
2 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 2
 
< 0.1%
0.995 24
0.4%
0.994 14
0.3%
0.993 16
0.3%
0.992 9
 
0.2%
0.991 10
0.2%
0.99 11
0.2%
0.989 9
 
0.2%
0.988 7
 
0.1%
0.987 7
 
0.1%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2491
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15988609
Minimum0
Maximum0.997
Zeros1447
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:16.732324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000228
Q30.091375
95-th percentile0.894
Maximum0.997
Range0.997
Interquartile range (IQR)0.091375

Descriptive statistics

Standard deviation0.30440707
Coefficient of variation (CV)1.9038996
Kurtosis1.1241682
Mean0.15988609
Median Absolute Deviation (MAD)0.000228
Skewness1.6767466
Sum882.57122
Variance0.092663662
MonotonicityNot monotonic
2022-11-29T17:48:16.831151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1447
 
26.2%
0.926 11
 
0.2%
0.907 11
 
0.2%
0.903 9
 
0.2%
0.918 9
 
0.2%
0.858 9
 
0.2%
0.908 8
 
0.1%
0.884 7
 
0.1%
0.881 7
 
0.1%
0.917 7
 
0.1%
Other values (2481) 3995
72.4%
ValueCountFrequency (%)
0 1447
26.2%
1.01 × 10-64
 
0.1%
1.02 × 10-62
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-61
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.08 × 10-66
 
0.1%
1.09 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.997 1
< 0.1%
0.991 1
< 0.1%
0.982 1
< 0.1%
0.98 1
< 0.1%
0.979 1
< 0.1%
0.978 2
< 0.1%
0.976 1
< 0.1%
0.97 2
< 0.1%
0.969 2
< 0.1%
0.968 1
< 0.1%

liveness
Real number (ℝ)

Distinct1239
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19720192
Minimum0.013
Maximum0.992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:16.935326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.013
5-th percentile0.05039
Q10.089675
median0.127
Q30.259
95-th percentile0.601
Maximum0.992
Range0.979
Interquartile range (IQR)0.169325

Descriptive statistics

Standard deviation0.17457278
Coefficient of variation (CV)0.88524888
Kurtosis5.5087604
Mean0.19720192
Median Absolute Deviation (MAD)0.05465
Skewness2.2251274
Sum1088.5546
Variance0.030475655
MonotonicityNot monotonic
2022-11-29T17:48:17.037202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.109 51
 
0.9%
0.11 47
 
0.9%
0.107 45
 
0.8%
0.112 45
 
0.8%
0.104 44
 
0.8%
0.121 44
 
0.8%
0.111 43
 
0.8%
0.106 43
 
0.8%
0.114 42
 
0.8%
0.108 41
 
0.7%
Other values (1229) 5075
91.9%
ValueCountFrequency (%)
0.013 1
 
< 0.1%
0.0169 1
 
< 0.1%
0.0184 1
 
< 0.1%
0.0214 1
 
< 0.1%
0.0219 3
0.1%
0.0229 1
 
< 0.1%
0.0233 2
< 0.1%
0.0236 1
 
< 0.1%
0.0246 1
 
< 0.1%
0.025 1
 
< 0.1%
ValueCountFrequency (%)
0.992 1
< 0.1%
0.989 2
< 0.1%
0.987 1
< 0.1%
0.985 1
< 0.1%
0.984 1
< 0.1%
0.981 1
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.977 2
< 0.1%
0.976 1
< 0.1%

valence
Real number (ℝ)

Distinct1088
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53768549
Minimum0
Maximum0.996
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:17.138100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.101
Q10.33
median0.556
Q30.751
95-th percentile0.93305
Maximum0.996
Range0.996
Interquartile range (IQR)0.421

Descriptive statistics

Standard deviation0.25768823
Coefficient of variation (CV)0.47925457
Kurtosis-1.0117229
Mean0.53768549
Median Absolute Deviation (MAD)0.207
Skewness-0.16798299
Sum2968.0239
Variance0.066403222
MonotonicityNot monotonic
2022-11-29T17:48:17.239404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 27
 
0.5%
0.96 16
 
0.3%
0.963 16
 
0.3%
0.777 15
 
0.3%
0.738 15
 
0.3%
0.966 14
 
0.3%
0.765 14
 
0.3%
0.383 14
 
0.3%
0.752 14
 
0.3%
0.353 14
 
0.3%
Other values (1078) 5361
97.1%
ValueCountFrequency (%)
0 3
0.1%
0.0183 1
 
< 0.1%
0.0253 1
 
< 0.1%
0.0279 1
 
< 0.1%
0.0284 1
 
< 0.1%
0.0295 1
 
< 0.1%
0.0297 1
 
< 0.1%
0.0299 1
 
< 0.1%
0.0306 1
 
< 0.1%
0.0307 1
 
< 0.1%
ValueCountFrequency (%)
0.996 1
 
< 0.1%
0.983 2
 
< 0.1%
0.981 4
0.1%
0.979 1
 
< 0.1%
0.978 4
0.1%
0.977 2
 
< 0.1%
0.976 1
 
< 0.1%
0.975 3
0.1%
0.974 2
 
< 0.1%
0.973 5
0.1%

tempo
Real number (ℝ)

Distinct5301
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.84042
Minimum34.535
Maximum217.943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:17.341777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum34.535
5-th percentile78.80215
Q196.422
median114.959
Q3135.988
95-th percentile175.98105
Maximum217.943
Range183.408
Interquartile range (IQR)39.566

Descriptive statistics

Standard deviation29.48948
Coefficient of variation (CV)0.24814351
Kurtosis0.0034937758
Mean118.84042
Median Absolute Deviation (MAD)19.3985
Skewness0.63465715
Sum655999.14
Variance869.62944
MonotonicityNot monotonic
2022-11-29T17:48:17.441803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.007 3
 
0.1%
116.699 3
 
0.1%
120.051 3
 
0.1%
95.926 3
 
0.1%
120.979 3
 
0.1%
131.741 3
 
0.1%
103.027 3
 
0.1%
102.3 3
 
0.1%
68.535 3
 
0.1%
143.8 3
 
0.1%
Other values (5291) 5490
99.5%
ValueCountFrequency (%)
34.535 1
< 0.1%
46.185 1
< 0.1%
47.485 1
< 0.1%
49.285 1
< 0.1%
50.823 1
< 0.1%
50.838 1
< 0.1%
51.29 1
< 0.1%
51.833 1
< 0.1%
52.793 1
< 0.1%
53.765 1
< 0.1%
ValueCountFrequency (%)
217.943 1
< 0.1%
217.872 1
< 0.1%
207.639 1
< 0.1%
207.435 1
< 0.1%
207.424 1
< 0.1%
207.285 1
< 0.1%
206.679 1
< 0.1%
206.247 1
< 0.1%
205.733 1
< 0.1%
205.487 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct4616
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256210.99
Minimum19533
Maximum1711800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:17.545764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19533
5-th percentile130038.55
Q1202753.25
median246720
Q3289907
95-th percentile404253.95
Maximum1711800
Range1692267
Interquartile range (IQR)87153.75

Descriptive statistics

Standard deviation103439.76
Coefficient of variation (CV)0.40372879
Kurtosis26.853498
Mean256210.99
Median Absolute Deviation (MAD)43506.5
Skewness3.3850812
Sum1.4142847 × 109
Variance1.0699783 × 1010
MonotonicityNot monotonic
2022-11-29T17:48:17.916644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228867 6
 
0.1%
233533 5
 
0.1%
175600 4
 
0.1%
239733 4
 
0.1%
221867 4
 
0.1%
287733 4
 
0.1%
243333 4
 
0.1%
256333 4
 
0.1%
239227 4
 
0.1%
227067 4
 
0.1%
Other values (4606) 5477
99.2%
ValueCountFrequency (%)
19533 1
< 0.1%
26027 1
< 0.1%
30533 1
< 0.1%
34973 1
< 0.1%
35640 1
< 0.1%
37267 1
< 0.1%
38533 1
< 0.1%
39496 1
< 0.1%
43507 1
< 0.1%
43960 1
< 0.1%
ValueCountFrequency (%)
1711800 1
< 0.1%
1491040 1
< 0.1%
1354000 1
< 0.1%
1318893 1
< 0.1%
1249000 1
< 0.1%
1187987 1
< 0.1%
1186507 1
< 0.1%
1176738 1
< 0.1%
1110240 1
< 0.1%
1052133 1
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.2 KiB
4
5032 
3
 
400
5
 
59
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5520
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 5032
91.2%
3 400
 
7.2%
5 59
 
1.1%
1 29
 
0.5%

Length

2022-11-29T17:48:18.000857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:48:18.090049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 5032
91.2%
3 400
 
7.2%
5 59
 
1.1%
1 29
 
0.5%

Most occurring characters

ValueCountFrequency (%)
4 5032
91.2%
3 400
 
7.2%
5 59
 
1.1%
1 29
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5032
91.2%
3 400
 
7.2%
5 59
 
1.1%
1 29
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5032
91.2%
3 400
 
7.2%
5 59
 
1.1%
1 29
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5032
91.2%
3 400
 
7.2%
5 59
 
1.1%
1 29
 
0.5%

chorus_hit
Real number (ℝ)

Distinct5475
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.999358
Minimum0
Maximum235.06074
Zeros16
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:18.163378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.838719
Q128.0651
median36.51268
Q348.628155
95-th percentile78.145939
Maximum235.06074
Range235.06074
Interquartile range (IQR)20.563055

Descriptive statistics

Standard deviation19.94163
Coefficient of variation (CV)0.48638882
Kurtosis9.7022962
Mean40.999358
Median Absolute Deviation (MAD)9.90707
Skewness2.2212432
Sum226316.46
Variance397.66859
MonotonicityNot monotonic
2022-11-29T17:48:18.263291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
0.3%
24.17331 2
 
< 0.1%
21.28488 2
 
< 0.1%
68.31115 2
 
< 0.1%
30.85216 2
 
< 0.1%
9.68289 2
 
< 0.1%
18.76857 2
 
< 0.1%
43.23772 2
 
< 0.1%
59.02142 2
 
< 0.1%
32.08712 2
 
< 0.1%
Other values (5465) 5486
99.4%
ValueCountFrequency (%)
0 16
0.3%
6.51436 1
 
< 0.1%
8.1691 1
 
< 0.1%
8.27708 1
 
< 0.1%
8.50805 1
 
< 0.1%
8.51677 1
 
< 0.1%
8.57397 1
 
< 0.1%
8.65094 1
 
< 0.1%
9.47083 1
 
< 0.1%
9.68289 2
 
< 0.1%
ValueCountFrequency (%)
235.06074 1
< 0.1%
224.07663 1
< 0.1%
208.48624 1
< 0.1%
207.55451 1
< 0.1%
177.38599 1
< 0.1%
172.29658 1
< 0.1%
171.06546 1
< 0.1%
169.58862 1
< 0.1%
169.11724 1
< 0.1%
165.93523 1
< 0.1%

sections
Real number (ℝ)

Distinct49
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.128261
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2022-11-29T17:48:18.357452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median11
Q313
95-th percentile18
Maximum69
Range68
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4850255
Coefficient of variation (CV)0.40303023
Kurtosis22.276636
Mean11.128261
Median Absolute Deviation (MAD)2
Skewness3.0411811
Sum61428
Variance20.115454
MonotonicityNot monotonic
2022-11-29T17:48:18.457425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10 717
13.0%
11 693
12.6%
9 641
11.6%
12 611
11.1%
8 467
8.5%
13 443
8.0%
7 371
6.7%
14 355
6.4%
15 224
 
4.1%
6 219
 
4.0%
Other values (39) 779
14.1%
ValueCountFrequency (%)
1 3
 
0.1%
2 13
 
0.2%
3 29
 
0.5%
4 78
 
1.4%
5 130
 
2.4%
6 219
 
4.0%
7 371
6.7%
8 467
8.5%
9 641
11.6%
10 717
13.0%
ValueCountFrequency (%)
69 1
< 0.1%
65 1
< 0.1%
57 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
49 1
< 0.1%
47 2
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%

target
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.2 KiB
0
2760 
1
2760 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2760
50.0%
1 2760
50.0%

Length

2022-11-29T17:48:18.541266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:48:18.612898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2760
50.0%
1 2760
50.0%

Most occurring characters

ValueCountFrequency (%)
0 2760
50.0%
1 2760
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2760
50.0%
1 2760
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2760
50.0%
1 2760
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2760
50.0%
1 2760
50.0%

Interactions

2022-11-29T17:48:13.580541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:58.840921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.959547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.078284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.181298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.314637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.055871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.196802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.362027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.742724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.892930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.064884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.283703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.662816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:58.925566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.043966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.163108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.265937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.383689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.140552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.274955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.446715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.842617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.977748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.149551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.352756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.754453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.009916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.113009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.247635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.350602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.468491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.225235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.359289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.531087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.920753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.062236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.249881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.453053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.846344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.088071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.206789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.332458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.435308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.553029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.309913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.452326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.615742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.005450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.146874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.349752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.526738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.934512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.172751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.291644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.417032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.519569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.637709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.394575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.543965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.700387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.105762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.240638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.450060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.615385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.019060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.257413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.375971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.510804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.613333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.722563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.472735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.644242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.794164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.174807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.325292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.528203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.700240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.103715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.341763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.460643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.595473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.697996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.816339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.557407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.729100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.878812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.275119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.409954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.628495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.784412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.204024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.426436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.545309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.680118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.782648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.901012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.657733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.813752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.963504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.359795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.494632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.712842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.869143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.288698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.511092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.645611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.764799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.867218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:04.609801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.742131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.914038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.048256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.444801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.594923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.797518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.953788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.373338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.604858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.730300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.833827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.951559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:04.701008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.826835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.998682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.132493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.522960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.679598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.897796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.031924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.458007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.689509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.814959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:01.934134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.051855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:04.792727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:05.927136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.092444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.232747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.623232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.779611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:11.982444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.325785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.558317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.789811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.915276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.018482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.136543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:04.886505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.011834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.177089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.550903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.707906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.879916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.098767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.417433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:14.636471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:47:59.859253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:00.993430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:02.096634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:03.220859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:04.955669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:06.096493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:07.261737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:08.642952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:09.792596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:10.964584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:12.183412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:48:13.490562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-29T17:48:18.674921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:48:18.828374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:48:18.981462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:48:19.132574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:48:19.267003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:48:19.359108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:48:14.774621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:48:14.975146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
0Misty RosesAstrud Gilbertospotify:track:50RBM1j1Dw7WYmsGsWg9Tm0.5270.3161-15.76910.03100.6930000.0069900.16800.543116.211158840453.8952360
1Never EverAll Saintsspotify:track:5FTz9qQ94PyUHETyAyfYZN0.7380.5411-5.48510.03110.5590000.0000000.04920.309134.187387573432.16853161
2Soul SermonGregg Karukasspotify:track:6m24oe3lk1UMxq9zq4iPFi0.7360.4190-10.66210.03000.6930000.4950000.08090.26593.982237267442.0536990
3Clarinet Marmalade - LiveAlton Purnellspotify:track:5FOXuiLI6knVtgMUjWKj6x0.5650.5945-13.08610.06460.6550000.9260000.67500.763114.219375933480.99693100
4До смерті і довше - Drum & Base and Rock RemixSkryabinspotify:track:6CxyIPTqSPvAPXfrIZczs40.5130.7604-10.07710.03550.0000170.0033900.15300.961153.166430653425.57331200
5Cuspa Cerveja...Os Pedrerospotify:track:6IYGvJanVieBEtJaO4IGfc0.1660.9852-4.52910.11900.1470000.1790000.54000.609170.04565667428.1331140
6Baby-Baby-BabyTLCspotify:track:1zTuB57LYZa7xu7KUH8kF00.6790.59711-8.60100.04390.0913000.0006730.04870.900184.174315040420.93007151
7I Missed The BusKris Krossspotify:track:793gh4IXh7mQsMBhvcJRlt0.8110.6344-10.40800.07140.0046900.0094300.22400.273107.915179160454.7873091
8The Comfort ZoneVanessa Williamsspotify:track:7okbmgA8lRBGl5limZ7LFM0.5280.2343-15.78410.02830.6410000.0000000.18300.19195.911218733446.33298101
9Hardcore Rules25 Ta Lifespotify:track:75UjNrkncJ1idxVzlxkxLW0.2070.9896-5.56410.21800.0001180.1340000.20000.323200.46580987449.5264330
trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
5510(You Drive Me) CrazyBritney Spearsspotify:track:1DSJNBNhGZCigg9ll5VeZv0.7480.9390-4.28800.03410.0534000.0000000.32000.960104.001198067419.2942691
5511I'm OutstandingShaquille O'Nealspotify:track:1bNdtZeHdLoi5KuilQTqVn0.7690.75711-9.52310.16900.0077000.0140000.81900.713100.057247507457.76135111
5512Love Like ThisFaith Evansspotify:track:7MQywXGHEev7JmwwIzMcao0.7670.5510-7.32810.06160.0036400.0000000.04510.796100.904275707432.35991111
5513No Guns, No MurderRayvonspotify:track:54zbUGqw8JRk020wLRWlHl0.7970.68310-14.29900.21900.0816000.0000000.29600.88597.984277027428.96569151
5514DeeperBossspotify:track:6PZ4laM20qkvIrgji3NLsK0.6310.8486-6.43500.33800.0550000.0000000.38500.67983.155241560422.23523131
5515(You're A) Go NowhereReagan Youthspotify:track:4e86fqSFhqRQk3Z9hm7XHt0.3960.7959-6.07000.23400.0009280.0002090.18200.762152.94382107430.3410960
5516La Fiebre de NormaLa Castañedaspotify:track:43DFcnOZprnVlAFKwgBJ3e0.6210.6559-6.28100.03090.0506000.0062600.09370.690134.167211653434.89506100
5517Good TimesEdie Brickellspotify:track:6UPfnVoOq3y3BvapBIKs8J0.5620.31410-15.21300.02980.4400000.0000110.10600.571166.847189827421.11763101
5518InaneKMFDMspotify:track:2Ao3Wi4raEOQfKQiU9EU8y0.6220.7817-6.08010.03680.0001010.7550000.38300.214120.051330053447.13558110
5519You Can Make History (Young Again)Elton Johnspotify:track:3ca91BX2k7GSzEUsx1mPgI0.6640.7392-9.00510.02620.1060000.0542000.33300.45892.257293973442.50341141